1. NExT
  2. Petrotechnical
  3. Applied Statistical Modeling and Data Analytics

Applied Statistical Modeling and Data Analytics

Level: Skill | Type: Practical Training with Software | Discipline: Advanced Level Special Topics

This course discusses:

•Visualizing univariate, bivariate and multivariate data
•Fitting simple and multiple linear regression models to observed data
•Developing a non-parametric regression model from given data
•Reducing data dimensionality with Principal Component Analysis
•Grouping data with k-means and hierarchical clustering
•Identifying classification boundary between clusters using discriminant analysis
•Applying machine learning techniques (e.g., random forest, gradient boosting machine, support vector regression, kriging model, neural networks) for predictive modeling
•Generating decision rules with classification tree analysis
•Translating model input uncertainty into uncertainty in model predictions using Monte Carlo simulation and analytical alternatives
•Analyzing input-output dependencies from Monte-Carlo simulation results
•Creating an experimental design and fitting a response surface to the results
•Hybrid modeling combining data-driven and physics-based models

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